Neural Network
Neural networks are computational models inspired by the structure and function of the brain, primarily aimed at approximating complex functions and solving diverse problems through learning from data. Current research emphasizes improving efficiency and robustness, exploring novel architectures like sinusoidal neural fields and hybrid models combining neural networks with radial basis functions, as well as developing methods for understanding and manipulating the internal representations learned by these networks, such as through hyper-representations of network weights. These advancements are driving progress in various fields, including computer vision, natural language processing, and scientific modeling, by enabling more accurate, efficient, and interpretable AI systems.
Papers
Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
Xiaoran Cheng, Sen Na
Trustworthy Conceptual Explanations for Neural Networks in Robot Decision-Making
Som Sagar, Aditya Taparia, Harsh Mankodiya, Pranav Bidare, Yifan Zhou, Ransalu Senanayake
Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons
Farhad Pourkamali-Anaraki
Neuromorphic Spintronics
Atreya Majumdar, Karin Everschor-Sitte
Machine listening in a neonatal intensive care unit
Modan Tailleur (LS2N, Nantes Univ - ECN, LS2N - équipe SIMS), Vincent Lostanlen (LS2N, LS2N - équipe SIMS, Nantes Univ - ECN), Jean-Philippe Rivière (Nantes Univ, Nantes Univ - UFR FLCE, LS2N, LS2N - équipe PACCE), Pierre Aumond (UMRAE)
Tracking the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020 using human mobility-based graph neural network
Zhiyue Xia, Kathleen Stewart
Evaluating probabilistic and data-driven inference models for fiber-coupled NV-diamond temperature sensors
Shraddha Rajpal, Zeeshan Ahmed, Tyrus Berry
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding
Yu Song, Liyuan Han, Bo Xu, Tielin Zhang
Robust Training of Neural Networks at Arbitrary Precision and Sparsity
Chengxi Ye, Grace Chu, Yanfeng Liu, Yichi Zhang, Lukasz Lew, Andrew Howard
FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection
Jialuo Chen, Jingyi Wang, Xiyue Zhang, Youcheng Sun, Marta Kwiatkowska, Jiming Chen, Peng Cheng
Layerwise Change of Knowledge in Neural Networks
Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang
Fair CoVariance Neural Networks
Andrea Cavallo, Madeline Navarro, Santiago Segarra, Elvin Isufi
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice
Tian-Yi Zhou, Matthew Lau, Jizhou Chen, Wenke Lee, Xiaoming Huo
Edge-Wise Graph-Instructed Neural Networks
Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini, Francesco Vaccarino
Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example
Mikhail Kiselev
Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
Jiajing Guan, Howard Elman